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• Organizational and teamwork skills • English C1 (or equivalent) • Research experience in the theory of quantum programming languages (expertise in topics listed above is an asset) Website for additional job
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Inria, the French national research institute for the digital sciences | Palaiseau, le de France | France | 2 months ago
of publications in quantum information theory. Familiarity with Bell nonlocality, operator algebras, SDP relaxations of polynomial optimization problems, quantum correlation protocols, experimental physics and / or
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dislocation dynamics (DDD) code from TRIDIS, NUMODIS, or OpenDIS, based on the candidate's programming skills (Fortran, C++, or C). - Validate the DDD-FFT coupling required to optimize calculations
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 4 days ago
applied mathematics, control, or related fields, with knowledge in several of the following areas: Graph theory and network modeling Dynamical systems and physical modeling (ODE/PDE, multi-agent systems
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characterization techniques. • Experience in simulation and molecular modeling. • Prior knowledge of density functional theory (DFT) and experience with DFT codes are highly desirable. Skills and abilities
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count regime, joint gamma/neutrino studies of VHE data. The requited person will achieve these research studies within the H.E.S.S., CTAO or KM3NeT collaboration. - Gammapy developer (code, test
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Research Agency, we are recruiting a Post-Doctoral Researcher in Raman Optical Activity F/H. This project, based on a close relationship between theory and experiment, aims to verify and understand
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-of-the-art theory and modeling. For that purpose, CEA/IRIG is actively developing the “TB_Sim” code. TB_Sim is able to describe very realistic qubit structures down to the atomic scale if needed using
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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models are deeply rooted in real-world biological data. The collaborative approach allows for the development of predictive models that bridge the gap between theory and experiment, with a focus on high